导航

日志

Healthcare Analytics

Like other application domains, doctors and medical specialists are now interested in exploiting healthcare data for better healthcare and disease prevention, and also better utilization of resources. However, the data although is deposited in a central, possibly national scale, storage systems, the data is owned by different healthcare providers who will guide the data zealously, due to its commercial value and confidentiality. Notwithstanding, each can still perform analysis on what each owns. We have been working with doctors in the last three years, and have identified various research issues. We are examining the current dataset and the historical datasets. Figure 1 shows the applications and infrastructure of healthcare analytics.

Some ongoing analytics we build on GEMINI:

1. Disease Progression Modelling

Disease progression modelling (DPM) is to employ computational methods to model the progression of a specific disease. With the help of DPM, we can detect a certain disease early and therefore, manage the disease better. For chronic diseases, using DPM can effectively delay patients' deterioration and improve patients' healthcare outcomes. Therefore, we can provide helpful reference information to doctors for their judgment and benefit patients in the long run. Deep learning model can capture the time-relatedness between irregular, consecutive visits of patients and the unlinear relationship between heterogeneous medical features to further predict patients' future severity.

2. Readmission

In medical practice, effective and timely interventions to reduce hospital readmissions are often expensive to perform. Understanding the reasons why patients readmit and predicting avoidable readmissions will help target the patients most likely to benefit, thus will improve the quality of life for patients and reduce the financial cost for hospitals. Deep learning methods take the advantage from learning representations of time series, extracting phenotypes from complex EMR data, so as to improve the prediction of avoidable readmissions.

3. Treatment recommendation system for clinicians

Through various levels of automation in diagnosis model and prognosis prediction, the system may improve the medical treatment process in different degrees from helping doctors to make decisions (e.g. visualize cohort information) to outperforming doctors in treatment planning and recommendation.

4. Real-time surgical operation suggestions

Lots of emergency situations may happen during surgical operations. Armed with real time sensors and reinforcement learning models, the machine may be able to deliver a better contingency plan in a much shorter time and with more accurate decision making.